Overview and Classification of Swarm Intelligence-Based Nature-Inspired Computing Algorithms and Their Applications in Cancer Detection and Diagnosis

Abstract

AbstractWith the emergence of nature-inspired computing (NIC) techniques, researchers have understood and modeled solutions for realistic and complex problems. NIC, a branch of artificial intelligence worked on the transferring of knowledge from natural phenomenon to engineered systems, applicable in various fields. Although there are many techniques to be used in disease diagnosis, NIC algorithms are very efficient and have gained more attention to problems of modern research. In recent years, these algorithms gained popularity in the detection and diagnosis of cancer, a life-threatening disease that led to a high rate of mortality in individuals. Swarm Intelligence (SI), one of the most used NIC-based algorithms motivated by the collection of social insects’ behavior such as termites, bees, wasps, etc. helps in solving various bioinformatics-related problems. Herein, a chapter has presented various nature-inspired computing intelligence algorithms, with more focus on different types of SI-based nature-inspired algorithms that focus on principles, developments, and application scopes. Further, the chapter has also described applications of SI-based algorithms in detecting and diagnosing different stages and types of cancers. Finally, it has focused on strengths and limitations followed by future directions of these techniques in cancer diagnosis.KeywordsArtificial intelligenceNature-inspired computingCancerSwarm intelligence-based algorithmsParticle swarm optimization